Improved AVO fluid detection and lithology discrimination using Lamé petrophysical parameters; “λρ”,“μρ”,&amp;“λ/μ fluid stack”, from P and S inversions

Goodway, Bill; Chen, Taiwen; Downton, Jon

doi:10.1190/1.1885795

Cited by 393 publications

(203 citation statements)

References 13 publications

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“…Reservoir sands plotted as low μρ and I p , and high R clusters than shale. This low μρ for oil sands is in variance with the high μρ associated with hydrocarbon sands from petrophysical studies [7]. Low μρ value suggests that the reservoir sands are probably unconsolidated and might be associated with minor shale intrusions in the investigated field.…”

Section: Discussion Of Resultsmentioning

confidence: 40%

Enhanced Prospect Definition Using Well and 4D Seismic Data in a Niger Delta Field

Dagogo¹,

Ehirim²,

Ebeniro³

2016

IJG

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We carried out time-lapse analysis in a producing Niger Delta X-field, by first investigating the response and sensitivity of rock properties/attributes to lithology and pore fill in 3-D cross plot domain and by Gassmann's fluid substitution modeling. Furthermore, 4-D seismic data were inverted into acoustic impedance volumes through model based inversion scheme. This served as input into a multi-attribute neural network algorithm for the extraction of rock attribute volumes based on the results of the petrophysical log analysis. Subsequently, horizon slices of rock properties/ attributes were extracted from the inverted seismic data and analyzed. In this way, we mapped hydrocarbon depleted wells in the field, and identified probable by-passed hydrocarbon zones. Thus, the integration of well and time lapse seismic (4-D) data in reservoir studies has remarkably improved information on the reservoir economic potential, and enhanced hydrocarbon recovery factor.

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Section: Discussion Of Resultsmentioning

confidence: 40%

Enhanced Prospect Definition Using Well and 4D Seismic Data in a Niger Delta Field

Dagogo¹,

Ehirim²,

Ebeniro³

2016

IJG

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“…The Lambda-mu-rho approach was proposed by Goodway et al in 1997, where he introduced the Lame parameters ( λ and μ) and density ρ as the core parameters of the Simultaneous Inversion new approach. …”

Section: Methodsmentioning

confidence: 99%

Application of Simultaneous Inversion Method to Predict the Lithology and Fluid Distribution in “X” Field, Malay Basin

Yoong

Lubis

Ghosh

2016

IOP Conf. Ser.: Earth Environ. Sci.

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“…The average proportions from the wells are used as the basis for the prior probabilities in the Bayesian facies classification. mu-rho (LMR) attributes (Goodway et al, 1997) are then computed from the inverted volumes.…”

Section: Simultaneous Inversionmentioning

confidence: 99%

Prestack inversion and multiattribute analysis for porosity, shale volume, and sand probability in the Havert Formation of the Goliat field, southwest Barents Sea

et al. 2017

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An integrated innovative multidisciplinary approach has been used to estimate effective porosity (PHIE), shale volume (V sh ), and sand probability from prestack angle gathers and petrophysical well logs within the Lower Triassic Havert Formation in the Goliat field, Southwest Barents Sea. A rock-physics feasibility study revealed the optimum petrofacies discriminating ability of extended elastic impedance (EEI) tuned for PHIE and V sh . We then combined model-based prestack inversion outputs from a simultaneous inversion and an EEI inversion into a multilinear attribute regression analysis to estimate absolute V sh and PHIE seismic attributes. The quality of the V sh and PHIE prediction is shown to increase by integrating the EEI inversion in the workflow. Probability distribution functions and a priori petrofacies proportions extracted from the well data are then applied to the V sh and PHIE volumes to obtain clean and shaly sand probabilities. A tectonic-controlled point-source depositional model for the Havert Formation sands is then inferred from the extracted sand bodies and the seismic geomorphological character of the different attributes. IntroductionThe past two decades have seen a significant increase in the use of quantitative seismic interpretation methods to characterize the subsurface. Quantitative seismic interpretation techniques are increasingly being implemented into reservoir characterization schemes to minimize hydrocarbon exploitation risks. The measured seismic data respond to the contrasts in the effective elastic properties in the subsurface area of investigation. However, the main goal is usually aimed at characterizing the different lithofacies, porosity distribution, and fluid content responsible for the effective elastic response. These underlying properties are only indirectly measured using the available proxies in the seismic data, which are then transformed to the geologic variables of interest using rock physics.Direct hydrocarbon indicators (DHIs) from seismic data are not always successful because the seismic amplitude is a composite response of the saturated rock. Therefore, it is important to spend as much time characterizing the lithology response before fluid anomaly hunting. Reservoir quality assessment from seismic, well log, and laboratory data requires an integrated approach involving geology, petrophysics, rock physics,

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Improved AVO fluid detection and lithology discrimination using Lamé petrophysical parameters; “λρ”,“μρ”,&“λ/μ fluid stack”, from P and S inversions

Cited by 393 publications

References 13 publications

Enhanced Prospect Definition Using Well and 4D Seismic Data in a Niger Delta Field

Enhanced Prospect Definition Using Well and 4D Seismic Data in a Niger Delta Field

Application of Simultaneous Inversion Method to Predict the Lithology and Fluid Distribution in “X” Field, Malay Basin

Prestack inversion and multiattribute analysis for porosity, shale volume, and sand probability in the Havert Formation of the Goliat field, southwest Barents Sea

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